Adaptive Neural Network Sliding Mode Control for the Fuel Cell Air Supply System

被引：0
|
作者：
Zhang C.-L. [1 ]
Li H. [1 ]
Dong M.-L. [1 ]
Zhang S.-J. [1 ]
机构：
[1] School of Mechanical Engineering and Automation, Northeastern University, Shenyang
来源：
Dongbei Daxue Xuebao/Journal of Northeastern University | 2022年 / 43卷 / 09期
关键词：
adaptive law; oxygen excess ratio; polymer electrolyte membrane fuel cell(PEMFC); radial basis function(RBF)neural network; sliding mode control;
D O I：
10.12068/j.issn.1005-3026.2022.09.008
中图分类号：
学科分类号：
摘要：
The air supply system of polymer electrolyte membrane fuel cell(PEMFC) is vulnerable to the negative impact of parameter uncertainties and external disturbances， and it is difficult to achieve high-precision mathematical modeling and robust control. An adaptive neural network sliding mode controller is designed to adjust the oxygen excess ratio of PEMFC air supply system to its optimal reference value so as to maintain the maximum system output net power and avoid oxygen starvation. The radial basis function(RBF) neural network is employed to approximate the dynamics of the unmodeled system online without prior information of the boundary of external disturbances and model parameter perturbations. To ensure the stability of the closed-loop system， the adaptive laws of neural network weights and sliding mode gains are derived by Lyapunov theory. The numerical simulation results demonstrate that the designed controller not only improves the dynamic behavior of the oxygen excess ratio control， but also effectively alleviates the large overshoot and chattering of the control input. © 2022 Northeastern University. All rights reserved.
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页码：1270 / 1276
页数：6
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